F.A.S.T. package for summarizing ROOT TTrees
Project description
fast-carpenter
Turns your trees into tables (ie. reads ROOT TTrees, writes summary Pandas DataFrames)
fast-carpenter can:
- Be controlled using YAML-based config files
- Define new variables
- Cut out events or define phase-space "regions"
- Produce histograms stored as CSV files using multiple weighting schemes
- Make use of user-defined stages to manipulate the data
Powered by:
- AlphaTwirl (presently): to run the dataset splitting
- Atuproot: to adapt AlphaTwirl to use uproot
- uproot: to load ROOT Trees into memory as numpy arrays
- fast-flow: to manage the processing config files
- fast-curator: to orchestrate the lists of datasets to be processed
- coffee: to help the developer(s) write code
Installation
Can be installed from pypi:
pip install --user fast-carpenter
or if you want to be able to edit code in this repo:
pip install --user -e git+https://gitlab.cern.ch/fast-hep/public/fast-carpenter.git#egg=fast_carpenter --src .
Note that to use this repository and the main fast_carpenter command, you normally shouldn't need to be able to edit this codebase;
in most instances the full analysis should be describable with just a config file, and in some cases custom, analysis-specific stages to create more tricky variables for example.
Also note that if you install this with pip, the main executable, fast_carpenter, will only be available everywhere if include the directory ~/.local/bin in your PATH variable.
Documentation
Basic usage:
- Build a description of the datasets you wish to process using the
fast_curatorcommand from the fast-curator package. - Write a description of what you want to do with your data (see documentation below).
- Run things:
fast_carpenter datasets.yaml processing.yaml
You can use the built-in help as well for more info:
fast_carpenter --help
Talk:
which partially act as documentation:
Example analysis
The fast_cms_public_tutorial shows an example analysis that uses this package.
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